Operational Marketing AI | Peter Mahoney from Plannuh

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Peter Mahoney 0:00
We allow our customers to literally throw those documents at us. So they can email them, they can drag and drop them onto the platform in our system ingests all the data in organizes it for them automatically.

Alexander Ferguson 0:19
Peter, I’m excited to be able to chat with you today to begin, can you describe your company in five seconds? What do you guys do?

Peter Mahoney 0:27
We automate marketing, planning and budgeting. How’s that?

Alexander Ferguson 0:31
Oh, that was good. Most people actually have struggled with being able to describe it in five seconds.

Peter Mahoney 0:37
Well it’s pretty simple concept. And the shocking thing, of course, is you would have assumed that someone has figured this out before, and they haven’t, which is why we exist.

Alexander Ferguson 0:46
So tell me that that let’s dive a little bit into the problem that you initially saw. What was that?

Peter Mahoney 0:51
Well, I saw this problem personally. Because before founding the company, I was the CMO of a large public software company about a $2 billion software company. And I struggled mightily trying to figure out where the money was, was it being applied toward the right things? Was I achieving my goals? And could I optimize my plan? And then the big sort of pit in my stomach? Problem was, could I prove my value of someone asked me to? So could I actually demonstrate that we were delivering value to the business? And it’s an extremely hard thing to do, surprisingly, for a marketing team?

Alexander Ferguson 1:29
It’s that show me exactly the data of what all the money we spent, where did it go? How did it generate? Anything? And you’re like? Yes,

Peter Mahoney 1:39
exactly. It’s really shocking in the problem comes from the fact that there’s a Venus and Mars issue with marketing and finance. They just speak different languages, and marketing things in goals in themes and objectives in campaigns. And in finance, people think about their chart of accounts and their departments, and the different kind of fiscal reports that they have to come within. And getting the two of them to speak together is is a real challenge. And if they have a really good strong fluid conversation, then you get great business performance out of your marketing, if not, no guarantees.

Alexander Ferguson 2:23
So then you saw this problem, you’re like, let’s solve it. Let’s talk then about the technology, the platform, how does it work? Maybe even just giving us a nice use case of one of your clients?

Peter Mahoney 2:33
Yeah, great, great example is, as people engage with our platform, the first thing they do is they build their plan. Today, if you build a marketing plan, it’s on a PowerPoint deck, and a Word doc and some sticky notes and some whiteboards. It’s just not a living system. So you actually build your plan in planner, we actually helped them do that. So it’s up and running. And then they track their progress and monitor how the plan is doing, are they achieving the results that they expected in the campaigns, are they spending what they said they’re gonna spend, and they the system tracks that along the way and gives them sort of real time view of how they’re executing their plan. And then if weird things happen, like a giant global pandemic, and everything changes, it’s extremely difficult to adapt when there’s a sticky note in PowerPoint deck and in whiteboard driven plan. But if you have a distributed organization and have an online system that manages the connectedness between these things, it’s far easier to adjust along the way. And and that’s how people tend to use our system today,

Alexander Ferguson 3:42
perfectly positioned in today’s distributed environment to be able to keep working. Tell me that is there a good size company that can truly take advantage of your platform or whether like an average budget range that you found and worked with? Well,

Peter Mahoney 3:58
yeah, and in fact, it’s it’s an interesting that you brought up budget range, because that’s how we think about that’s how we think about our the applicability of our platform. And in general, people spend us at the low end around half a million dollars annual in their discretionary spend. And in the high end, we have people are spending $50 million or more. They’re giant giant companies where we’re probably not the best solution for them if you’re a Coca Cola or something like that. But for most companies, especially we think of midsize enterprise as the right kind of customers, and we’ve got people who are in financial services in tech, we’ve got legal firms, we’ve got a little bit of everything. It’s it’s a horizontal problem that we’re solving, but we tend to think we we built the platform for mostly mid sized companies.

Alexander Ferguson 4:55
Let’s dive in a bit more to some of the feature sets of the technology itself. How is it different from let me just use some spreadsheets or whiteboard it or put in a Google Doc or presentation? How does it work?

Peter Mahoney 5:07
It’s a great question. And one of the things that is challenging about trying to solve this problem is that most most data in a company, it’s coming from some system somewhere. But because you’re planning, it’s prospective, it’s something that’s going to happen out in the future. So they’re often is in a system. It’s a weird collection of different documents and quotes from vendors and estimates and contracts. So one of the things that’s unique about planner is that we allow our customers to literally throw those documents at us so they can email them, they can drag and drop them onto the platform. And our system ingests all the data and organizes it for them automatically. And we figured out early on that this was a really important piece of the puzzle, because what it does is it gives you a much earlier and more accurate view of what is expected to happen. And that means that if you are starting to vary from what your plan is, we have a much clearer view of when that variance is happening. So getting in ingesting that data is really important. And the wonky answer to that is we leverage a Assisted Learning Technologies. So it’s basically a form of artificial intelligence. And basically, it uses artificial intelligence data models and people. So it uses what’s called a human in a loop model, which allows you to really, perfectly solve a problem because you have a person who can handle the trickiest bits of the things. But you can do it super efficiently because the algorithms do most of it. It’s the way a lot of systems work that are dealing with complex problems. And that’s how we solve the problem it planner.

Alexander Ferguson 6:55
awful to have the human in the loop, but assisted so that AI can get most of the way that’s a nice service also that that they don’t have to worry about getting the data in there. Is there any particular industries you fat as far as that are using your platform more as far as the CMOS?

Peter Mahoney 7:10
Well, the industries that have adopted planet are fairly diverse these days. That being said, there were a few clusters of of industries where we’ve seen more adoption. Tech is one just because tech tends to adopt early in general. So that kind of happens. And it especially in the early stage of a company, you leverage your personal network and sort of the orders of magnitude away from that. And in, we came from tech, so you tend to leverage those kinds of relationships. But as I mentioned, there are a bunch of law firms who are using our platform, several banks are using our platform, we’ve got some consumer facing companies, we’ve got an auto dealership, we’ve got a dental practice group, we’ve got a little bit of everything. And it’s because the problem is quite universal, of sort of managing, managing your plans, optimizing it and improving the value of marketing.

Alexander Ferguson 8:09
How is your platform and service different from other options out there?

Peter Mahoney 8:15
I mentioned this idea of this automation and human in a loop kind of capability that’s fundamentally different from anything else on the market. The the what that allows us to do so the that’s a technical jargony answer to something that may not be that interesting to someone who’s a business person. They what it allows us to do, though, is really rapidly get to that point where we’re delivering value. So we get customers up and running and benefiting from the platform within a couple of weeks. And that is quite rare when you’re dealing with enterprise software that’s dealing with a lot of different data and systems and things like that. The other thing that we do is that the system really codifies expertise at some level. So our goal is to have the system be this giant brain that’s sort of the super expert marketer in the cloud. But it’s backed up by marketers who’ve got many, many decades of experience that can apply their human capabilities on top of what the AI is doing. So it’s this sort of accelerated augmented expertise that you get as a benefit as a marketer, which means you get people who can look at your plans, validate that it’s right, give you some good benchmarks and best practices. And in at the same time, do it really fast and get it to scale quite quickly because of this AI technology we use to get you up and running.

Alexander Ferguson 9:44
What other analytics or AI solutions do you have or are looking at at putting in where once the data is in there, it actually is providing insight and ideas or even suggestions on how you should be managing or adjusting your your marketing plan. and budget,

Peter Mahoney 10:01
you’re thinking about it exactly the right way. Because when you get the data in there and organized that way, that’s step one. And the next step is to start to do some things that can leverage the organization and understanding of how that data fits together across different companies. So one thing I briefly mentioned in my last comment is benchmarking. So one of the things we can do is, we can say, your plan looks a little bit like most of the people who are your peers, but it’s different in these ways. And that’s a really useful step in we can do that very accurately. Because we’ve got a little bit of a trick, we have this opportunity to leverage data science to automatically categorize the data inside your plan, because the problem with most sort of peer benchmarks that happen with marketing is that it’s a human exercise, which means you’ve got to each marketer has to say, well, this is what I’m doing. And I’m spending this much in this area and that much in the other area. But they all think about it differently. They define your categories differently. And we found a way to automate that using some deep learning technology, which allows us to sort of look at all the metadata around what they’re doing and spending and organizing for them in a normalized way. So what that means is that we can pretty accurately tell you the signature of your plan, and how it varies based based on the peers you may be working with. So that’s one thing. And that’s, that’s a short term thing that we’re doing sort of in beta right now, which is exciting. The second thing that we’re really excited about as we get more data in more scale, is the thing that you were alluding to before a minute ago is the idea of providing some very specific recommendations. So we have some humans who look at it, and we can look at our reports and data and say, Aha, you have a strategy of doing you have a strategy of thought leadership in inbound marketing, but you really haven’t invested anything in SEO, you might want to look at that. The goal is to have the system actually make those recommendations and do that in a very detailed way at scale.

Alexander Ferguson 12:15
The business model that you have is it is a monthly yearly kind of plan that people can sign up for.

Peter Mahoney 12:21
Yeah, it’s a it’s an annual subscription. For for the system. It’s a typical SAS model. And the one thing that we did that’s a little bit different from what others have done in this business model is are, we don’t price by the number of users that are out there, we actually price sort of like a wealth manager, we price based on the amount of budget under management. Because what we’re doing in it’s a tiny, tiny fraction, it’s actually quite cost effective solution, we start at about $500 a month, as an example. So it’s really in that if you have a relatively small budget, if you have a $500,000 budget, it’s quite small, and then it gets less as a percentage of your budget pretty dramatically as the budgets increase. So we we we price that way. And the advantage of doing that is that it one, it’s the investment is paired with the value that you’re going to derive because it’s really proportion to what you’re spending and how much more efficient we can make that spend. And then the second thing is, what it allows you to do is not limit usage. So what that means is that anybody who can get value from the data can immediately access the system, and there’s no extra price. And that that’s an all inclusive kind of thing. And a quick example of where that might be useful is I mentioned the relationship between marketing people and finance people, by giving finance people a view into what marketing is planning to do, they forge a better relationship. And then part of what we do is we spit out from our data, you know, a really beautifully formatted sort of campaign timeline calendar that people can share with others in the company. So it’s really helpful to see sort of what campaigns are coming, that that kind of stuff allows you to do a much more better job communicating what’s going on between marketing and other departments.

Alexander Ferguson 14:16
Where can people go to learn more, and what’s a good first step for them to take?

Peter Mahoney 14:20
I the best source of all information is plannuh.com. And it’s plannuh.com. And in a good step is there’s lots of great information on the website. We’re always happy to to talk to people who are interested in there’s a little opportunities, a little button on there that where you can set up a personalized demo and we’ll walk you through so we recommend that that’s a that definitely gives you the best opportunity to see if plan is going to be useful and be a good fit for you.

Alexander Ferguson 14:53
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